Journal of Information Processing Systems

  • 제15권4호
  • /
  • Pages.724-736
  • /
  • 2019
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

Dynamic Thermal Rating of Overhead Transmission Lines Based on GRAPES Numerical Weather Forecast

  • Yan, Hongbo (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Wang, Yanling (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Zhou, Xiaofeng (Dept. of Mechanical and Electrical engineering, Weihai Vocational College) ;
  • Liang, Likai (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Yin, Zhijun (Shandong Inspur Software Company Limited) ;
  • Wang, Wei (School of Electrical Engineering and Automation, Shandong University of Science and Technology)
  • 투고 : 2017.09.05
  • 심사 : 2018.10.30
  • 발행 : 2019.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Dynamic thermal rating technology can effectively improve the thermal load capacity of transmission lines. However, its availability is limited by the quantity and high cost of the hardware facilities. This paper proposes a new dynamic thermal rating technology based on global/regional assimilation and prediction system (GRAPES) and geographic information system (GIS). The paper will also explore the method of obtaining any point meteorological data along the transmission line by using GRAPES and GIS, and provide the strategy of extracting and decoding meteorological data. In this paper, the accuracy of numerical weather prediction was verified from the perspective of time and space. Also, the 750-kV transmission line in Shaanxi Province is considered as an example to analyze. The results of the study indicate that dynamic thermal rating based on GRAPES and GIS can fully excavate the line power potential without additional cost on hardware, which saves a lot of investment.

키워드

참고문헌

  1. Q. P. Zhang, Z. Y. Qian, "Study on real-time dynamic capacity-increase of transmission line," Power System Technology, vol. 29, no. 19, pp. 1138-1143, 2005.
  2. M. W. Davis, "A new thermal rating approach: the real time thermal rating system for strategic overhead conductor transmission lines, Part I: General description and justification of the real time thermal rating system," IEEE Transactions on Power Apparatus and Systems, vol. 96, no. 3, pp. 803-809, 1977. https://doi.org/10.1109/T-PAS.1977.32393
  3. J. F. Hall and A. K. Deb, "Prediction of overhead transmission line ampacity by stochastic and deterministic models," IEEE Transactions on Power Delivery, vol. 3, no. 2, pp. 789-800, 1988. https://doi.org/10.1109/61.4319
  4. D. A. Douglass and A. A. Edris, "Real-time monitoring and dynamic thermal rating of power transmission circuits," IEEE Transactions on Power Delivery, vol. 11, no. 3, pp. 1407-1418, 1996. https://doi.org/10.1109/61.517499
  5. D. M. Greenwood, J. P. Gentle, K. S. Myers, P. J. Davison, I. J. West, J. W. Bush, G. L. Ingram, and M. C. Troffaes, "A comparison of real-time thermal rating systems in the US and the UK," IEEE Transactions on Power Delivery, vol. 29, no. 4, pp. 1849-1858, 2014. https://doi.org/10.1109/TPWRD.2014.2299068
  6. S. C. E. Jupe, D. Kadar, G. Murphy, M. G. Bartlett, and K. T. Jackson, "Application of a dynamic thermal rating system to a 132kV distribution network," in Proceedings of 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, Manchester, UK, 2011, pp. 1-8.
  7. C. W. Zhang and X. Qiu, "Study on dynamic capacity increase for overhead lines," Guangdong Electric Power, vol. 25, no. 2, pp. 57-61, 2012. https://doi.org/10.3969/j.issn.1007-290X.2012.02.013
  8. G. Liu, B. Ruan, J. Lin, M. Yang, M. Zhang, and Z. Xu, "Steady-state model of thermal circuit method for dynamic overhead lines rating," High Voltage Engineering, vol. 39, no. 5, pp. 1107-1113, 2013. https://doi.org/10.3969/j.issn.1003-6520.2013.05.013
  9. IEEE Standard for Calculating the Current-Temperature Relationship of Bare Overhead Conductors, IEEE Standard 708-2012, 2013.
  10. R. Alberdi, E. Fernandez, I. Albizu, V. Valverde, M. T. Bedialauneta, and K. J. Sagastabeitia, "Statistical methods and weather prediction for ampacity forecasting in smart grids," in Proceedings of 2016 IEEE PES PowerAfrica, Livingstone, Zambia, 2016, pp. 21-25.
  11. D. Chen and X. Shen, "Recent progress on GRAPES research and application," Journal of Applied Meteorological Science, vol. 17, no. 6, pp. 773-777, 2006. https://doi.org/10.3969/j.issn.1001-7313.2006.06.014
  12. L. Zhao, W. Shen, H. Xiao, B. Wang, J. Sun, M. Wei, J. Li, and Y. Shen, "The application of high performance computing technology in meteorological field," Journal of Applied Meteorological Science, vol. 27, no. 5, pp. 550-558, 2016.
  13. G. Cheng, Y. N. He, and T. X. Yue, "Effects of climatic conditions and soil properties on Cabernet Sauvignon berry growth and anthocyanin profiles," Molecules, vol. 19, no. 9, pp. 13683-13703, 2014. https://doi.org/10.3390/molecules190913683
  14. Y. Liu, X. Ying, and F. Zhao, "Introduction to GRIB2 and GRIB2 decoding," Meteorological Science and Technology, vol. 34, no. S1, pp. 61-64, 2006.